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Optimizing Databricks Workloads
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Optimizing Databricks Workloads
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Overview of this book
Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud.
In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains.
By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.
Table of Contents (13 chapters)
Preface
Section 1: Introduction to Azure Databricks
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Chapter 1: Discovering Databricks
Chapter 2: Batch and Real-Time Processing in Databricks
Chapter 3: Learning about Machine Learning and Graph Processing in Databricks
Section 2: Optimization Techniques
Chapter 4: Managing Spark Clusters
Chapter 5: Big Data Analytics
Chapter 6: Databricks Delta Lake
Chapter 7: Spark Core
Section 3: Real-World Scenarios
Chapter 8: Case Studies
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